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About This Role
The Opportunity
Oracle Cloud Infrastructure is seeking a Master Principal or Distinguished Cloud Architect to join the Strategic Pursuits & Engagements team and help turn OCI’s infrastructure advantages into strategic customer wins.
As enterprise AI moves into production, customers must solve two increasingly important problems: continually lower the cost of delivering verified business outcomes and maintain resilient access to the models and capacity required to run their businesses. This creates an opportunity for OCI to differentiate through its infrastructure strengths—including compute and network economics, bare metal, Kubernetes, multicloud connectivity, and distributed cloud.
This is a senior, customer-facing technical leadership role. The successful candidate will help refine our strategy through direct market engagement, but their primary responsibility is to execute it with customers : identify opportunities where OCI can create a meaningful advantage, architect the solution, prove the differentiation, and help win the business.
What You’ll Do
Lead strategic pursuits.
Serve as a senior technical leader for some of OCI’s most important and complex opportunities, working directly with customer executives, architects, platform teams, and engineers from discovery through technical validation and decision.
Architect differentiated AI infrastructure.
Design production architectures spanning AI inference, Kubernetes, CPUs and GPUs, model serving, intelligent routing, multicloud, distributed cloud, edge, security, observability, and resilient execution across models, providers, regions, and capacity pools.
Turn OCI differentiation into customer value.
Connect OCI capabilities—including network economics, flexible compute, bare metal, multicloud connectivity, and distributed deployment—to measurable improvements in cost, performance, resilience, security, and operational control.
Prove it.
Move beyond presentations and architecture diagrams. Develop prototypes, benchmarks, demonstrations, and proofs of value that establish why an OCI-based architecture is better for the customer’s workload.
Shape and operationalize the strategy.
Use customer engagements to refine reference architectures, technical positioning, solution playbooks, and pursuit patterns. Work across OCI engineering, product, sales, partners, and field architecture to turn successful engagements into repeatable motions.
Be the technical authority in the room.
Operate comfortably from executive conversations about business outcomes and economics to deep technical sessions with principal engineers and architects. Challenge assumptions, navigate ambiguity, and build customer confidence through technical credibility.
What We’re Looking For
We are looking for someone with exceptional depth in cloud infrastructure and distributed systems who can apply that experience to the rapidly evolving enterprise AI landscape.
The ideal candidate brings significant expertise across several of the following:
- Large-scale cloud and distributed systems architecture
- Kubernetes and cloud-native platforms
- Compute, networking, and storage architecture
- CPU, GPU, and accelerator-based infrastructure
- AI/ML infrastructure, inference, and model serving
- Multicloud, distributed, sovereign, and edge architectures
- High availability and resilient system design
- Performance engineering and observability
- Cloud economics and FinOps
- Security, identity, governance, and policy
- Open-source infrastructure and automation
Experience with modern AI infrastructure technologies—such as Kubernetes, KServe, vLLM, Triton, Ray, LiteLLM, Envoy, Istio, OpenTelemetry, MLflow, Terraform/OpenTofu, AI gateways, and model platforms is highly relevant.
More important than expertise in any individual technology is the ability to understand complex systems, identify where infrastructure creates competitive advantage, and translate that advantage into a solution a customer will choose.
The Person We’re Looking For
This person combines the mindset of a principal engineer, field CTO, and pursuit architect .
They start with the customer’s business outcome rather than the product catalog. They think across the entire system rather than optimizing one component. They understand economics as well as architecture. They are comfortable with Oracle, open-source, third-party, and competing cloud technologies. And they can build and prove ideas rather than simply present them.
Above all, they know how to win complex technical decisions.
Success
Success is measured by customer impact:
- Strategic OCI opportunities advanced and won through technical leadership
- New and expanded OCI workloads created through differentiated architectures
- Technical proofs that materially change customer architecture or sourcing decisions
- Measurable customer improvements in economics, performance, resilience, or control
- Repeatable architectures and pursuit patterns created from successful engagements
The goal is straightforward: turn OCI’s infrastructure advantages into compelling customer outcomes—and turn those outcomes into strategic wins.
- Strategic OCI opportunities advanced and won through technical leadership
- New and expanded OCI workloads created through differentiated architectures
- Technical proofs that materially change customer architecture or sourcing decisions
- Measurable customer improvements in economics, performance, resilience, or control
- Repeatable architectures and pursuit patterns created from successful engagements
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Oracle, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Oracle AI Hiring
Oracle has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Architect, Research Scientist. Positions span Seattle, WA, US, Redwood City, CA, US, Seattle, WA.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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